class_docstr: Large-scale distributed sampling face recognition training Module, of course sampling is an option,
  There will be no loss of accuracy in sampling in large-scale identities training tasks, uses only
  8 NVIDIA RTX2080Ti to complete classification tasks with 10 millions of identities, 64 NVIDIA
  RTX2080Ti can complete classification tasks with 100 million of identities.
  
  See the original paper:
  https://arxiv.org/abs/2203.15565
  
  Parameters
  ----------
  symbol: Symbol
      Backbone symbol.
  fc7_model: Object
      Object of margin loss.
  memory_bank: Memory bank Object.
      Object of memory bank, which maintain local class centers and their momentum.
  memory_optimizer: Optimizer object.
      The updater of memory bank, default is sgd optimizer.
  logger:
docstr: :param params:
  :return:
docstr_tok: [":","param","params",":",":","return",":"]
code: def broadcast_parameters(params):
      """
      :param params:
      :return:
      """
  
      rank_0_dict = {}
  
      # Run broadcasts.
      for key, tensor in params.items():
          rank_0_dict[key] = hvd.broadcast(tensor, 0, key)
      return rank_0_dict
  
imports: import logging
  import warnings
  from collections import namedtuple
  import horovod.mxnet
  import mxnet
  import mxnet.ndarray
  import numpy
  from default import config
  from optimizer import DistributedOptimizer
label_desc: Use image node_id 3 for calling the SampleDistributeModule obj's underlying member method code with example usage: obj.broadcast_parameters(params) and returns: rank_0_dict